ABSTRACT : Economic activities and investments require resources and capital. Developing countries are
faced with limited resource; therefore‫ و‬the need to select proper projects is intensified. Our country is not an
exception to this rule, where lack of adequate resource allocation and planning has caused many projects to
come to a standstill. This paper reports on the implementation of an expert system, which takes advantage of
expertise in the area of granting loans to build a knowledge base. The system helps in making evaluations and
expediting activities. Furthermore, using this system, accuracy is improved, allowing managers to trust expert
decisions in granting or rejecting loan requests with more confidence. The proposed system uses a certainty
factor to determine the characteristics of the case and ultimately help managers make better decisions.

KEYWORDS: expert system, knowledge base, certainty factor, loan
I.

INTRODUCTION

In the modern world, especially in developing countries, large investments are made to facilitate
economic growth. Making investments requires financial resources, which are often controlled by banks and
provided for projects in form of loans. Identifying profitable projects is an important issue. Moreover, the
significance of rejecting or accepting loan requests in developing countries, including Iran, is known to
everyone. If clear rules, based on science, are used in evaluating plans and allocating resources, projects will not
remain unfinished nor become economically unjustifiable. This paper aims to assist banks decide whether to
accept or reject a particular loan request. Using the C Language Integrated Production System (CLIPS), we have
tried to build an expert system, to help increase accuracy and speed in evaluating loan requests. Although senior
management makes the ultimate decision, he does so based on experts’ opinions. There is a lack of such expert
systems in our baking industry. Previous works in this area have not considered a certainty factor. This factor
gives managers a percentage of certainty to base their decisions on. Moreover, fuzzy logic can further enhance
the system.

II.

OVERVIEW OF THE SOFTWARE

When deciding on a loan request, various factors are taken into account. Senior managers, as the
ultimate decision makers, consider the following factors[1].
1[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]

Available resources and the bank’s strategies (variable among banks).
Each of the mentioned factors can be evaluated on a qualitative scale (poor, average, good, and
excellent). However, such a scale is not appropriate, since individuals can interpret each degree differently. A
certainty factor can help eliminate such misinterpretations, making decisions more reliable. Table Ι: shows the
rating scale for the study[1].
5-

Table Ι: Rating Scale
Evaluation result:
obtaining a minimum
score from the expert
system (on a scale of 1
to 5)

In this manner, minimum scores and the certainty factor can help in evaluating a project and ultimately
deciding on the loan request. Since the certainty factor is expressed in terms of factors, it will streamline the
decision making process. Each of the four considered factors has certain dependencies. For instance, project
feasibility depends on four other factors. These factors are assigned degrees of certainty based on questions
answered by the user. The degrees are either directly assigned by the user or determined from his answers.
Using all factors and questions, an overall degree of certainty for each of the four factor is determined. After
determining the certainty of each factor, we have the following rule[2,3].
( management disagreement) (Edesign fair)(trust poor)(Evaluation good)

(printout t "your loan request Reject")
The rule has an overall Certainty Factor (CF), which is obtained from previous records of the bank. In order to
determine the certainty of rejection, minimum CFs are multiplied by the overall CF.
CF (loan request Rejected) = min (
,
,
,
)*
The determined percentage is then used to express the certainty associated with rejecting a request.
In order to make a final decision, we need to have some rules and relations between the factors, which are
provided by experts. The tree in Figure (2) classifies the rules, so that they can be more easily understood.

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Implementation of Expert System for Lending With….
III.

MECHANISMS

The system is written using the CLIPS programming language. In the first step, the user is given
several questions to answer. The final result is expressed using a certainty factor. Unlike previous works which
rate requests as poor, average, or excellent, the proposed system provide a numerical basis for evaluations.

Expert systems are very common in developed countries. Such systems are very important in
evaluating projects. More accurate and unbiased evaluations will lead to a higher rate of project completion.
In developing countries, especially Iran, limited resources force investors to evaluate projects before accepting
to provide money. However, despite this fact, many projects remain unfinished, which shows the need for
further studies in this area. Intelligent and expert systems can greatly improve the quality of decisions and
improve performance. In this paper, an expert system was implemented which helped banks decide whether or
not to grant a loan request. The proposed system can be improved using fuzzy logic.